Equipment monitoring can avoid costly repairs. This can be done by analyzing time series data acquired by sensors. One method treats each multivariate data point at time t independently. That method does not use sliding windows over time. Because that method does not analyze data in time windows, the method cannotdetect “collective anomalies,” which are anomalies in the dynamics of a variable, i.e., changes over time. That method does not compute any feature vectors, or representation of the data. The method simply compares raw time series test data with raw training data.
Another method assumes multivariate time series can be modeled locally as a vector autoregressive (AR) model. This is a fairly restrictive assumption. That method first learns a distribution of AR model parameters for each time window of the training data. During testing, for each time window, the AR model parameters are estimated and the probability of these parameters are computed from the previously learned probability distribution. The distribution learned by that method uses a restrictive autoregressive assumption.